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About This Role
*Hungry, humble and smart? If you have these qualities, we want you on the team.*
Job Summary:
The Senior Agentic AI Engineer is the operational owner of the Constituent Data Layer (CDL) and the safe, secure rollout of AI agents across CaterTrax® constituent surfaces.
Reporting to the Director, Enterprise Architecture as a member of the Data Layer group, this engineer manages the CDL schema, the agent registry, and the policy engine that governs which agents can access which data under which conditions, and instruments the data flywheel that returns outcomes from agent actions back into the layer.
The role partners with the Director of Strategic Solutions on Brief\-to\-agent translation, with the Director, Product and Solutions on engineering intake, with platform engineering on runtime, and with Finance on data flywheel ROI metrics. Without this role, agents would be built ad hoc across teams with no shared governance; this seat ensures they are not.Job Description:
ESSENTIAL DUTIES AND RESPONSIBILITIES
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CONSTITUENT DATA LAYER OWNERSHIP
- Manage the Constituent Data Layer schema, including versioning, integrity, and evolution as constituent surfaces expand.
- Own the agent registry: the authoritative record of every agent operating across CaterTrax surfaces.
- Build and operate the policy engine governing which agents can access which data under which conditions.
- Establish and maintain shared governance so agents are built on the CDL rather than ad hoc across teams.
AGENT DEVELOPMENT \& SAFE ROLLOUT
- Design, build, and deploy AI agents across constituent surfaces on the shared platform substrate.
- Implement guardrails, evaluation harnesses, and monitoring for agent behavior in production.
- Define and operate the rollout process for new agents, including staged release, human oversight, and rollback.
- Lead incident response for agent behavior issues, including root\-cause analysis and remediation.
DATA FLYWHEEL \& MEASUREMENT
- Instrument the data flywheel that returns outcomes from agent actions back into the Constituent Data Layer.
- Partner with Finance to define and report data flywheel ROI metrics.
- Ensure agent telemetry supports both product improvement and executive decision\-making.
CROSS\-FUNCTIONAL PARTNERSHIP
- Partner with the Director of Strategic Solutions on Brief\-to\-agent translation, converting constituent problem framing into agent designs.
- Partner with the Director, Product and Solutions on engineering intake and sequencing.
- Partner with platform engineering on agent runtime, deployment, and operational readiness.
ENGINEERING EXCELLENCE
- Uphold engineering standards, code review practices, and documentation quality across all Data Layer work.
- Embed security and privacy by design into every agent and data\-layer decision.
QUALIFICATIONS AND EXPERIENCE INCLUDES
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EDUCATION
Bachelor's degree in Computer Science, Software Engineering, or a related technical discipline required.
Master's degree preferred.
EXPERIENCE
Required:
- 6\+ years of professional software engineering experience, including 2\+ years building LLM\-based or AI\-powered systems in production.
- Hands\-on experience with retrieval\-augmented generation (RAG), vector databases, and prompt/agent design patterns.
- Strong data modeling and API design skills, including schema governance in a shared\-platform context.
- Experience implementing evaluation, monitoring, and guardrails for AI systems in production.
- Proficiency in Python and/or a modern backend language used in production services.
Preferred:
- Experience with agent orchestration frameworks, agent registries, or policy/authorization engines.
- Experience in foodservice technology, hospitality tech, or B2B SaaS platforms.
- Familiarity with responsible AI practices, AI governance frameworks, and data privacy regulations.
SKILLS AND COMPETENCY REQUIREMENTS
- Ownership: Treats the Constituent Data Layer as a product with constituents, not a project with tasks.
- Safety mindset: Designs for what agents must never do before optimizing what they can do.
- Pragmatic shipping: Delivers working, governed capability iteratively rather than perfect systems slowly.
- Cross\-functional translation: Moves fluently between constituent problem language and engineering design.
- Documentation discipline: Leaves every schema, agent, and policy legible to the next engineer.
- Curiosity: Tracks the rapidly evolving agentic AI landscape and applies it judiciously.
TECHNOLOGY SKILLS
The Senior Agentic AI Engineer is expected to bring deep hands\-on proficiency across the following technology domains.
AI \& MACHINE LEARNING PLATFORMS
- Large language model (LLM) platforms: OpenAI / Azure OpenAI, Anthropic Claude, AWS Bedrock, or equivalent.
- Agent orchestration frameworks, function/tool calling, and multi\-agent patterns.
- Retrieval\-augmented generation (RAG) architecture and vector database platforms.
- AI ops tooling for model monitoring, versioning, drift detection, and deployment pipelines.
CLOUD \& INFRASTRUCTURE
- Cloud platforms: proficiency in Microsoft Azure, with fluency across AWS and Google Cloud.
- Containerization and orchestration: Docker and Kubernetes.
- Serverless and event\-driven architecture patterns.
DATA \& ANALYTICS
- SQL, relational, and vector databases.
- Data pipeline and transformation tooling.
- Schema design, data contracts, and data governance practices.
SOFTWARE ENGINEERING \& DEVOPS
- Source control and collaboration: Git, GitHub, or GitLab, including PR workflows and code review practices.
- CI/CD pipeline platforms: GitHub, Azure DevOps.
- API design and management; identity and access patterns (OAuth 2\.0, SSO/SAML).
- Agile and project management tooling: Jira, Confluence (Atlassian suite experience preferred).
PRODUCTIVITY \& COLLABORATION
- Microsoft 365 suite: Teams, SharePoint, Excel, and PowerPoint.
- AI productivity tools: Microsoft Copilot, Anthropic Claude, or similar for personal and team enablement.
COMPENSATION
Salary range: $140,000 \- $170,000, dependent upon experience
PHYSICAL DEMANDS
While performing the duties of this job, the employee is regularly required to sit; use hands to operate a computer, copier, fax or other office equipment. The employee must occasionally lift and/or move up to 15 pounds.
WORK ENVIRONMENT
Assumes a hybrid work environment located in Rochester, New York.
While performing the duties of this job, the noise level in the work environment is usually quiet to moderate.
TRAVEL
Up to 10%, Domestic (as required)
Worker Type:
RegularNumber of Openings Available:
1*Be part of something big. Apply to join our team today*
Salary Context
This $140K-$170K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At CaterTrax, Inc., this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($155K) sits 28% below the category median. Disclosed range: $140K to $170K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
CaterTrax, Inc. AI Hiring
CaterTrax, Inc. has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Rochester, NY, US. Compensation range: $170K - $170K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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